Abstract: A computer-implemented method and apparatus for facilitating a provisioning of advertisements to customers effects display of one or more advertisements on a Web domain. Each displayed advertisement is configured to offer advertisement related assistance to customers visiting the Web domain. A selection input provided by a customer on an advertisement is received. In response to the receipt of the selection input, the system facilitates customer engagement with a customer support representative associated with an enterprise related to the advertisement. The customer engagement facilitated through the advertisement is configured to provide the advertisement related assistance to the customer.

Abstract: A computer-implemented method and an apparatus for personalizing customer interaction experiences receives an input corresponding to at least one of a business objective and a customer interaction channel. A customer classification framework is selected based on the input. The customer classification framework is associated with a plurality of persona types, where each persona type is associated with a set of behavioral traits. A persona type for a customer is predicted from among the plurality of persona types during an interaction on the customer interaction channel. A propensity of the customer to perform at least one action is predicted based on the persona type. A provisioning of personalized interaction experience to the customer is facilitated based on the predicted propensity of the customer to perform the at least one action.

Abstract: In accordance with an example embodiment a computer-implemented method and an apparatus for predicting and tracking of mood changes in textual conversations are provided. The method includes determining, by a processor, one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer. Changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation are tracked by the processor. Further, the method includes determining, by the processor, at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics.

Abstract: A computer-implemented method and an apparatus facilitate user engagement on enterprise interaction channels. Information related to a current journey of a user on one or more enterprise interaction channels is received. The user is categorized as one of a hot lead, a warm lead, and a non-hot lead based, at least in part, based on the received information related to the current journey of the user. If the user is categorized as the non-hot lead, a user interface (UI) displayed to the user is modified. The UI is modified to facilitate user engagement for converting the user from a non-purchasing entity to a purchasing entity.

Abstract: A computer-implemented method and an apparatus for personalizing customer interaction experiences receives an input corresponding to at least one of a business objective and a customer interaction channel. A customer classification framework is selected based on the input. The customer classification framework is associated with a plurality of persona types, where each persona type is associated with a set of behavioral traits. A persona type for a customer is predicted from among the plurality of persona types during an interaction on the customer interaction channel. A propensity of the customer to perform at least one action is predicted based on the persona type. A provisioning of personalized interaction experience to the customer is facilitated based on the predicted propensity of the customer to perform the at least one action.

Abstract: Embodiments of the invention relate to chat and, more particularly, to determining an that is to be action taken based on the type of chat session. The resolution of the chat is categorized to decide the necessary steps taken and also to monitor the agent's performance. A chat filter extracts relevant portions of a chat session. The relevant factors are taken into consideration and scored based on the feature vectors. A model is built and the type of resolution is determined. An analysis of the chat session is then performed taking into consideration several factors.

Abstract: User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions.

Abstract: The stages of an interaction between a potential customer (the user) and a sales representative (the agent) during a sales interaction are identified to understand the interaction factors that drive sales and, by doing so, to serve the customer better and thus increase sales. Initially, a user makes contact with an agent via a communications network. During the interaction, a dropping point is reached, i.e. the point in the interaction at which either the user or the agent ends the interaction. The dropping point and other interaction factors is analyzed. Based upon such analysis, various recommendations are made to the agents to improve the user's sales experience.

Abstract: In accordance with an example embodiment a computer-implemented method and an apparatus for predicting and tracking of mood changes in textual conversations are provided. The method includes determining, by a processor, one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer. Changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation are tracked by the processor. Further, the method includes determining, by the processor, at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics.

Abstract: In accordance with an example embodiment a computer-implemented method and an apparatus for predicting and tracking of mood changes in textual conversations are provided. The method includes determining, by a processor, one or more mood metrics in each of two or more chat stages of a real-time textual conversation between an agent and a customer. Changes in the one or more mood metrics across the two or more chat stages of the real-time textual conversation are tracked by the processor. Further, the method includes determining, by the processor, at least one action associated with the real-time textual conversation based on the changes in the one or more mood metrics.

Abstract: The stages of an interaction between a potential customer (the user) and a sales representative (the agent) during a sales interaction are identified to understand the interaction factors that drive sales and, by doing so, to serve the customer better and thus increase sales. Initially, a user makes contact with an agent via a communications network. During the interaction, a dropping point is reached, i.e. the point in the interaction at which either the user or the agent ends the interaction. The dropping point and other interaction factors is analyzed. Based upon such analysis, various recommendations are made to the agents to improve the user's sales experience.

Abstract: A computer-implemented method and apparatus for serving online communities of users includes effecting display of an interactive section on at least a portion of a user interface of a website upon receiving a user input indicative of a need for assistance. The interactive section is displayed to enable the user to post one or more queries to at least one relevant community of users from among a plurality of community of users associated with the website. Further, an interaction is facilitated between the user and one or more users associated with the at least one relevant community of users or between the user and the agent using the interactive section in response to posting of the one or more queries by the user.

Abstract: The stages of an interaction between a potential customer (the user) and a sales representative (the agent) during a sales interaction are identified to understand the interaction factors that drive sales and, by doing so, to serve the customer better and thus increase sales. Initially, a user makes contact with an agent via a communications network. During the interaction, a dropping point is reached, i.e. the point in the interaction at which either the user or the agent ends the interaction. The dropping point and other interaction factors is analyzed. Based upon such analysis, various recommendations are made to the agents to improve the user's sales experience.

Abstract: A computer-implemented method and apparatus for facilitating a provisioning of advertisements to customers effects display of one or more advertisements on a Web domain. Each displayed advertisement is configured to offer advertisement related assistance to customers visiting the Web domain. A selection input provided by a customer on an advertisement is received. In response to the receipt of the selection input, the system facilitates customer engagement with a customer support representative associated with an enterprise related to the advertisement. The customer engagement facilitated through the advertisement is configured to provide the advertisement related assistance to the customer.

Abstract: A computer-implemented method and an apparatus to facilitate building of prediction models from customer Web logs includes receiving a Web log including unstructured data and structured data corresponding to a customer's journey on a Website. The structured data in the Web log is used to generate structured variables and the unstructured data in the Web log is used to generate unstructured variables. The generated structured and unstructured variables are concatenated to form a session string, which serves as a textual representation of the customer's journey on the Website. The session string is subjected to text-based processing to generate a plurality of features. The plurality of features are used to build one or more prediction models for facilitating prediction of at least one response variable corresponding to the customers visiting the Website.

Abstract: The stages of an interaction between a potential customer (the user) and a sales representative (the agent) during a sales interaction are identified to understand the interaction factors that drive sales and, by doing so, to serve the customer better and thus increase sales. Initially, a user makes contact with an agent via a communications network. During the interaction, a dropping point is reached, i.e. the point in the interaction at which either the user or the agent ends the interaction. The dropping point and other interaction factors is analyzed. Based upon such analysis, various recommendations are made to the agents to improve the user's sales experience.

Abstract: User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions.

Abstract: A computer-implemented method and an apparatus facilitate user engagement on enterprise interaction channels. Information related to a current journey of a user on one or more enterprise interaction channels is received. The user is categorized as one of a hot lead, a warm lead, and a non-hot lead based, at least in part, based on the received information related to the current journey of the user. If the user is categorized as the non-hot lead, a user interface (UI) displayed to the user is modified. The UI is modified to facilitate user engagement for converting the user from a non-purchasing entity to a purchasing entity.

Abstract: The stages of an interaction between a potential customer (the user) and a sales representative (the agent) during a sales interaction are identified to understand the interaction factors that drive sales and, by doing so, to serve the customer better and thus increase sales. Initially, a user makes contact with an agent via a communications network. During the interaction, a dropping point is reached, i.e. the point in the interaction at which either the user or the agent ends the interaction. The dropping point and other interaction factors is analyzed. Based upon such analysis, various recommendations are made to the agents to improve the user's sales experience.

Abstract: User interactions are categorized into predefined hierarchical categories by classifying user interactions, such as queries, during a user interaction session by labeling text data into predefined hierarchical categories, and building a scoring model. The scoring model is then executed on untagged user interaction data to classify the user interactions into either action-based or information-based interactions.